Enhanced Elman Spike neural network optimized with Red Fox optimization algorithm for sugarcane yield grade prediction

IF 2.4 Q2 MULTIDISCIPLINARY SCIENCES
M. Deepanayaki, Vidyaathulasiraman
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引用次数: 0

Abstract

ABSTRACT In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). Initially, the sugar yield prediction dataset is taken. Then the input data are pre-processed by hybrid decomposition method that is morphological filtering and extended empirical wavelet transformation (MF-EEWT) to retrieve the missing values. These pre-processed outputs are given to feature selection methods. During the process of feature selection, Entropy – Kurtosis-based feature selection method (EKBFS) is applied. These extracted features are fed to EESNN, and then it classifies the sugarcane yield as low grade, medium grade, and high grade. Generally, EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast. To forecast the sugarcane production accurately, the Red Fox Optimization Algorithm (RFOA) is proposed. The proposed approach is carried out in Python; its performance is evaluated under performance metrics, such as precision, root mean square error, mean square error, mean absolute percentage error, convergence curve, and predicted percentage of changes in sugarcane yield during 2021–2027. The proposed SYGP-EESNN-RFOA framework attains higher accuracy of 27.5%, 16.65%, and 9.13%, 15.21% higher specificity compared with the existing methods. Graphical abstract In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast.
红狐优化算法优化的增强型Elman Spike神经网络用于甘蔗产量等级预测
摘要本文提出了用Red Fox优化算法优化的增强Elman Spike神经网络(EESNN)用于甘蔗产量等级预测(SYGD-EESNN-RFOA)。最初,采用糖产量预测数据集。然后通过形态学滤波和扩展经验小波变换(MF-EEWT)的混合分解方法对输入数据进行预处理,以检索缺失值。这些预处理的输出被提供给特征选择方法。在特征选择过程中,采用了基于熵-峰度的特征选择方法。这些提取的特征被输入EESNN,然后将甘蔗产量分为低等级、中等等级和高等级。通常,EESNN方法没有指示使用任何优化策略来计算最佳参数,以确保准确的甘蔗产量预测。为了准确预测甘蔗产量,提出了红狐优化算法(RFOA)。所提出的方法是在Python中执行的;其性能根据性能指标进行评估,如精度、均方根误差、均方误差、平均绝对百分比误差、收敛曲线和2021-2027年甘蔗产量变化的预测百分比。与现有方法相比,所提出的SYGP-EESNN-RFOA框架具有更高的准确性,分别为27.5%、16.65%和9.13%,特异性分别高15.21%。本文提出了用Red Fox优化算法优化的增强Elman Spike神经网络(EESNN)用于甘蔗产量等级预测(SYGD-EESNN-RFOA)。EESNN方法没有表明使用任何优化策略来计算最佳参数,以确保准确的甘蔗产量预测。
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来源期刊
Smart Science
Smart Science Engineering-Engineering (all)
CiteScore
4.70
自引率
4.30%
发文量
21
期刊介绍: Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials
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